Neil Ireson

Neil Ireson

Interests

My current research interests can broadly be defined as an exploration of Collective Intelligence, and specifically the extraction of information from social media streams. My initial research focused on the use of Machine Learning for Knowledge Acquisition, more recently I have developed Text Classification and Information Extraction techniques.

Workshop/Conference Deadlines

Projects

Current

Football Whispers is a new global football digital platform has launched as the world’s first transfer predictor, giving football fans around the world the inside track on player movements. The first of its kind, Football Whispers uses a complex algorithm that can predict the likelihood of multi-million-pound football transfers taking place.
Rumoured football transfers are given a Football Whispers ‘Unique Index Score’ on a scale of 1 to 5 as an indication of how likely a transfer is to happen - the higher the number calculated the more likely the player is to move clubs. Using multiple data points, the algorithm looks at the volume of conversation (chatter), the authority of the sources and the recency of the story, as well as a host of other factors.
The technology behind the platform is been developed in collaboration with the Department of Computer Science's Organisations, Information and Knowledge (OAK) research group and Klood Digital.

SETA is set to create a technology and methodology that will address the challenges above and change the way mobility is organised, monitored and planned in large metropolitan areas. The solution will be based on the management of high-volume, high-velocity, multi-dimensional, heterogeneous, cross-media, cross-sectoral data and information which is sensed, crowdsourced, acquired, linked, fused, and used to model mobility with a precision, granularity and dynamicity that is impossible with today’s technologies. Such models will be used to provide always-on, pervasive services to citizens and business, as well as decision makers to support safe, sustainable, effective, efficient and resilient mobility. Differently from other initiatives that tend to focus only on transport optimisation in the inner city, we will focus on intelligent and sustainable mobility in entire metropolitan areas. A metropolitan area is a geographical and socio-economic region consisting of a densely populated urban core (the city) and its surrounding region, i.e. the counties that refer to the city as place of work or entertainment - an area that can be a hundred times bigger than the inner city. Intelligent and sustainable mobility encompasses the smarter, greener and more efficient movement of people and goods; it provides a radical change from transport as a series of separate modal journeys to an integrated, reactive, intelligent, mobility system.

Previous

In order to harness environmental data and knowledge to effectively and efficiently manage water resources, WeSenseIt, will propose to develop ;a citizen observatory of water,which will allow citizens and communities to take on a new role in the information chain: a shift from the traditional one-way communication paradigm towards a two-way communication model in which citizens become active stakeholders in information capturing, evaluation and communication.

The main objective of WeKnowIt is to develop novel techniques for exploiting multiple layers of intelligence from user-contributed content, which together constitute Collective Intelligence, a form of intelligence that emerges from the collaboration and competition among many individuals, and that seem
ingly has a mind of its own.

On the web, cultural heritage content is everywhere, in traditional environments such as libraries, museums, galleries and audiovisual archives, but also in popular magazines and newspapers, in multiple languages and multiple media. The aim of the MultiMATCH project is to enable users to explore and interact with online accessible cultural heritage content, across media types and languages boundaries.

In accordance with the need to understand the process of knowledge use, the programme of AKT is based around six challenges to ease fundamental bottlenecks in the engineering and management of knowledge. Each of these bottlenecks occurs at a vital stage in the evolution of knowledge; Acquisition, Modelling, Reuse, Retrieval, Publishing and Maintenance. My work focused on the use of Machine Learning in Information Extraction for Knowledge Acquisition.

POESIA seeks to develop, test, evaluate and promote a fully open-source, and extensible, state of the art, filtering and catching software solution. POESIA will filter harmful content in several channels (Web, Email, News) combining innovative technologies to achieve more effective filtering than existing products. Filtering will cover a range of modes, including image filtering, natural language text filtering, URL, PICs and JavaScript filtering. The filter will initially be deployed in English, Italian and Spanish. To cover other European languages additional work is required and should be effectively possible because of the open-source model and the portability of the technical solutions.

pre 2000

Cao YJ, Ireson N, Bull L & Miles R:
Design of a Traffic Junction Controller using Classifier System and Fuzzy Logic.
In Proceedings of the Sixth International Conference on Computational Intelligence Theory and Applications, 1999.bibtex